ABSTRACT
INTRODUCTION: Pelvic floor muscle training (PFMT) for urinary incontinence (UI) is recommended in combination with biofeedback to visualize pelvic floor muscles. The focus is on non-invasive hand-held ultrasound (US) measurement methods for PFMT, which can be performed at home. Recently, self-performed US measurements in which the patient applies the US to themself have gradually spreading. This study aimed to develop an educational program for the biofeedback method using self-performed US and to evaluate its feasibility. METHODS: This study was an observational study. The ADDIE model (Analysis, Design, Development, Implementation, and Evaluation) was utilized to create an e-learning program for women aged ≥40 years with UI. Participants self-performed bladder US via e-learning, using a hand-held US device with a convex probe. The primary outcome was the number of times the bladder area was successfully extracted using an existing automatic bladder area extraction system. The secondary outcome was the total score of the technical evaluation of the self-performed US, which was evaluated across three proficiency levels. Descriptive statistics were conducted for participant characteristics, presenting categorical variables as percentages and continuous variables as means ± SD. RESULTS: We included 11 participants with a mean age of 56.2 years. Nine participants were able to record US videos, and two were unable to record bladder videos. Regarding the technical evaluation scores, all participants scored ≥80%; four had perfect scores. CONCLUSIONS: This study showed that transabdominal self-performed bladder US can be performed in 81.8% of women with UI in their 40-60s by using an e-learning program.
ABSTRACT
INTRODUCTION: Non-invasive biofeedback of pelvic floor muscle training (PFMT) is required for continuous training in home care. Therefore, we considered self-performed ultrasound (US) in adult women with a handheld US device applied to the bladder. However, US images are difficult to read and require assistance when using US at home. In this study, we aimed to develop an algorithm for the automatic evaluation of pelvic floor muscle (PFM) contraction using self-performed bladder US videos to verify whether it is possible to automatically determine PFM contraction from US videos. METHODS: Women aged ≥ 20 years were recruited from the outpatient Urology and Gynecology departments of a general hospital or through snowball sampling. The researcher supported the participants in their self-performed bladder US and videos were obtained several times during PFMT. The US videos obtained were used to develop an automatic evaluation algorithm. Supervised machine learning was then performed using expert PFM contraction classifications as ground truth data. Time-series features were generated from the x- and y-coordinate values of the bladder area including the bladder base. The final model was evaluated for accuracy, area under the curve (AUC), recall, precision, and F1. The contribution of each feature variable to the classification ability of the model was estimated. RESULTS: The 1144 videos obtained from 56 participants were analyzed. We split the data into training and test sets with 7894 time series features. A light gradient boosting machine model (Light GBM) was selected, and the final model resulted in an accuracy of 0.73, AUC = 0.91, recall = 0.66, precision = 0.73, and F1 = 0.73. Movement of the y-coordinate of the bladder base was shown as the most important. CONCLUSION: This study showed that automated classification of PFM contraction from self-performed US videos is possible with high accuracy.
